Literature DB >> 24848289

Feasibility of diffusional kurtosis tensor imaging in prostate MRI for the assessment of prostate cancer: preliminary results.

Michael Quentin1, Gael Pentang2, Lars Schimmöller3, Olga Kott4, Anja Müller-Lutz5, Dirk Blondin6, Christian Arsov7, Andreas Hiester8, Robert Rabenalt9, Hans-Jörg Wittsack10.   

Abstract

PURPOSE: To assess the feasibility of full diffusional kurtosis tensor imaging (DKI) in prostate MRI in clinical routine. Histopathological correlation was achieved by targeted biopsy.
MATERIALS AND METHODS: Thirty-one men were prospectively included in the study. Twenty-one were referred to our hospital with increased prostate specific antigen (PSA) values (>4ng/ml) and suspicion of prostate cancer. The other 10 men were volunteers without any history of prostate disease. DKI applying diffusion gradients in 20 different spatial directions with four b-values (0, 300, 600, 1000s/mm(2)) was performed additionally to standard functional prostate MRI. Region of interest (ROI)-based measurements were performed in all histopathologically verified lesions of every patient, as well as in the peripheral zone, and the central gland of each volunteer.
RESULTS: DKI showed a substantially better fit to the diffusion-weighted signal than the monoexponential apparent diffusion coefficient (ADC). Altogether, 29 lesions were biopsied in 14 different patients with the following results: Gleason score 3+3=6 (n=1), 3+4=7 (n=7), 4+3=7 (n=6), 4+4=8 (n=1), and 4+5=9 (n=2), and prostatitis (n=12). Values of axial (Kax) and mean kurtosis (Kmean) were significantly different in the tumor (Kax 1.78±0.39, Kmean 1.84±0.43) compared with the normal peripheral zone (Kax 1.09±0.12, Kmean 1.16±0.13; p<0.001) or the central gland (Kax 1.40±0.12, Kmean 1.44±0.17; p=0.01 respectively). There was a minor correlation between axial kurtosis (r=0.19) and the Gleason score.
CONCLUSION: Full DKI is feasible to utilize in a routine clinical setting. Although there is some overlap some DKI parameters can significantly distinguish prostate cancer from the central gland or the normal peripheral zone. Nevertheless, the additional value of DKI compared with conventional monoexponential ADC calculation remains questionable and requires further research.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Diffusion-weighted imaging; Kurtosis; MRI; Prostate Cancer

Mesh:

Year:  2014        PMID: 24848289     DOI: 10.1016/j.mri.2014.04.005

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  18 in total

1.  Updates in advanced diffusion-weighted magnetic resonance imaging techniques in the evaluation of prostate cancer.

Authors:  Hebert Alberto Vargas; Edward Malnor Lawrence; Yousef Mazaheri; Evis Sala
Journal:  World J Radiol       Date:  2015-08-28

2.  The value of diffusion kurtosis magnetic resonance imaging for assessing treatment response of neoadjuvant chemoradiotherapy in locally advanced rectal cancer.

Authors:  Jing Yu; Qing Xu; Jia-Cheng Song; Yan Li; Xin Dai; Dong-Ya Huang; Ling Zhang; Yang Li; Hai-Bin Shi
Journal:  Eur Radiol       Date:  2016-09-08       Impact factor: 5.315

3.  The histogram analysis of diffusion-weighted intravoxel incoherent motion (IVIM) imaging for differentiating the gleason grade of prostate cancer.

Authors:  Yu-Dong Zhang; Qing Wang; Chen-Jiang Wu; Xiao-Ning Wang; Jing Zhang; Hui Liu; Xi-Sheng Liu; Hai-Bin Shi
Journal:  Eur Radiol       Date:  2014-11-28       Impact factor: 5.315

4.  Machine learning-based analysis of MR radiomics can help to improve the diagnostic performance of PI-RADS v2 in clinically relevant prostate cancer.

Authors:  Jing Wang; Chen-Jiang Wu; Mei-Ling Bao; Jing Zhang; Xiao-Ning Wang; Yu-Dong Zhang
Journal:  Eur Radiol       Date:  2017-04-03       Impact factor: 5.315

Review 5.  The expanding landscape of diffusion-weighted MRI in prostate cancer.

Authors:  Andreas G Wibmer; Evis Sala; Hedvig Hricak; Hebert Alberto Vargas
Journal:  Abdom Radiol (NY)       Date:  2016-05

6.  Diffusion kurtosis imaging to assess correlations with clinicopathologic factors for bladder cancer: a comparison between the multi-b value method and the tensor method.

Authors:  Fang Wang; Hai-Ge Chen; Rui-Yun Zhang; Di Jin; Shuai-Shuai Xu; Guang-Yu Wu; Jian-Rong Xu
Journal:  Eur Radiol       Date:  2019-01-21       Impact factor: 5.315

7.  Repeatability of radiomics and machine learning for DWI: Short-term repeatability study of 112 patients with prostate cancer.

Authors:  Harri Merisaari; Pekka Taimen; Rakesh Shiradkar; Otto Ettala; Marko Pesola; Jani Saunavaara; Peter J Boström; Anant Madabhushi; Hannu J Aronen; Ivan Jambor
Journal:  Magn Reson Med       Date:  2019-11-08       Impact factor: 4.668

8.  Improved Characterization of Diffusion in Normal and Cancerous Prostate Tissue Through Optimization of Multicompartmental Signal Models.

Authors:  Christopher C Conlin; Christine H Feng; Ana E Rodriguez-Soto; Roshan A Karunamuni; Joshua M Kuperman; Dominic Holland; Rebecca Rakow-Penner; Michael E Hahn; Tyler M Seibert; Anders M Dale
Journal:  J Magn Reson Imaging       Date:  2020-10-31       Impact factor: 4.813

9.  Application of diffusion kurtosis tensor MR imaging in characterization of renal cell carcinomas with different pathological types and grades.

Authors:  Jie Zhu; Xiaojie Luo; Jiayin Gao; Saying Li; Chunmei Li; Min Chen
Journal:  Cancer Imaging       Date:  2021-03-16       Impact factor: 3.909

10.  Diffusion-tensor-based method for robust and practical estimation of axial and radial diffusional kurtosis.

Authors:  Yasuhiko Tachibana; Takayuki Obata; Hiroki Tsuchiya; Tokuhiko Omatsu; Riwa Kishimoto; Hiroshi Kawaguchi; Akira Nishikori; Koji Kamagata; Masaaki Hori; Shigeki Aoki; Hiroshi Tsuji; Tomio Inoue
Journal:  Eur Radiol       Date:  2015-10-07       Impact factor: 5.315

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